{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python for Finance (2nd ed.)\n",
    "\n",
    "**Mastering Data-Driven Finance**\n",
    "\n",
    "&copy; Dr. Yves J. Hilpisch | The Python Quants GmbH\n",
    "\n",
    "<img src=\"http://hilpisch.com/images/py4fi_2nd_shadow.png\" width=\"300px\" align=\"left\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Statistics (c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Machine Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "from pylab import mpl, plt\n",
    "import warnings; warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "plt.style.use('seaborn')\n",
    "mpl.rcParams['font.family'] = 'serif'\n",
    "np.random.seed(1000)\n",
    "np.set_printoptions(suppress=True, precision=4)\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unsupervised Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets.samples_generator import make_blobs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = make_blobs(n_samples=250, centers=4,\n",
    "                  random_state=500, cluster_std=1.25)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(X[:, 0], X[:, 1], s=50);\n",
    "# plt.savefig('../../images/ch13/ml_plot_01.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K-Means Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = KMeans(n_clusters=4, random_state=0)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "       n_clusters=4, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "       random_state=0, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_kmeans = model.predict(X)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 0, 3, 0, 2, 3, 3, 3, 0, 1, 1], dtype=int32)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_kmeans[:12]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y_kmeans,  cmap='coolwarm');\n",
    "# plt.savefig('../../images/ch13/ml_plot_02.png');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gaussian Mixtures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.mixture import GaussianMixture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = GaussianMixture(n_components=4, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianMixture(covariance_type='full', init_params='kmeans', max_iter=100,\n",
       "                means_init=None, n_components=4, n_init=1, precisions_init=None,\n",
       "                random_state=0, reg_covar=1e-06, tol=0.001, verbose=0,\n",
       "                verbose_interval=10, warm_start=False, weights_init=None)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_gm = model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 0, 3, 0, 2, 3, 3, 3, 0, 1, 1])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_gm[:12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(y_gm == y_kmeans).all()  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Supervised Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_samples = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = make_classification(n_samples=n_samples, n_features=2, n_informative=2,\n",
    "                           n_redundant=0, n_repeated=0, random_state=250)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.6876, -0.7976],\n",
       "       [-0.4312, -0.7606],\n",
       "       [-1.4393, -1.2363],\n",
       "       [ 1.118 , -1.8682],\n",
       "       [ 0.0502,  0.659 ]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[:5]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 2)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 1, 1])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[:5]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100,)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(x=X[:, 0], y=X[:, 1], c=y, cmap='coolwarm');\n",
    "# plt.savefig('../../images/ch13/ml_plot_03.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gaussian Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = GaussianNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB(priors=None, var_smoothing=1e-09)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.0041, 0.9959],\n",
       "       [0.8534, 0.1466],\n",
       "       [0.9947, 0.0053],\n",
       "       [0.0182, 0.9818],\n",
       "       [0.5156, 0.4844]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_proba(X).round(4)[:5]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = model.predict(X)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0,\n",
       "       0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0,\n",
       "       0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0,\n",
       "       0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1,\n",
       "       0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True,  True, False,  True,  True,  True,  True,\n",
       "        True, False,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True, False, False, False,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True, False,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True, False,  True, False,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True, False,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True, False,  True, False,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True, False,  True, False,  True,  True,  True,  True,\n",
       "        True])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred == y  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.87"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y, pred)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xc = X[y == pred]  \n",
    "Xf = X[y != pred]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fe86ef41510>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(x=Xc[:, 0], y=Xc[:, 1], c=y[y == pred],\n",
    "            marker='o', cmap='coolwarm')  \n",
    "plt.scatter(x=Xf[:, 0], y=Xf[:, 1], c=y[y != pred],\n",
    "            marker='x', cmap='coolwarm')  \n",
    "# plt.savefig('../../images/ch13/ml_plot_04.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LogisticRegression(C=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.011 , 0.989 ],\n",
       "       [0.7266, 0.2734],\n",
       "       [0.971 , 0.029 ],\n",
       "       [0.04  , 0.96  ],\n",
       "       [0.4843, 0.5157]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_proba(X).round(4)[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xc = X[y == pred]\n",
    "Xf = X[y != pred]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(x=Xc[:, 0], y=Xc[:, 1], c=y[y == pred],\n",
    "            marker='o', cmap='coolwarm')\n",
    "plt.scatter(x=Xf[:, 0], y=Xf[:, 1], c=y[y != pred],\n",
    "            marker='x', cmap='coolwarm');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = DecisionTreeClassifier(max_depth=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                       max_depth=1, max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
       "                       random_state=None, splitter='best')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.08, 0.92],\n",
       "       [0.92, 0.08],\n",
       "       [0.92, 0.08],\n",
       "       [0.08, 0.92],\n",
       "       [0.08, 0.92]])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_proba(X).round(4)[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.92"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xc = X[y == pred]\n",
    "Xf = X[y != pred]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(x=Xc[:, 0], y=Xc[:, 1], c=y[y == pred],\n",
    "            marker='o', cmap='coolwarm')\n",
    "plt.scatter(x=Xf[:, 0], y=Xf[:, 1], c=y[y != pred],\n",
    "            marker='x', cmap='coolwarm');\n",
    "# plt.savefig('../../images/ch13/ml_plot_05.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   depth | accuracy\n",
      "--------------------\n",
      "       1 |     0.92\n",
      "       2 |     0.92\n",
      "       3 |     0.94\n",
      "       4 |     0.97\n",
      "       5 |     0.99\n",
      "       6 |     1.00\n"
     ]
    }
   ],
   "source": [
    "print('{:>8s} | {:8s}'.format('depth', 'accuracy'))\n",
    "print(20 * '-')\n",
    "for depth in range(1, 7):\n",
    "    model = DecisionTreeClassifier(max_depth=depth)\n",
    "    model.fit(X, y)\n",
    "    acc = accuracy_score(y, model.predict(X))\n",
    "    print('{:8d} | {:8.2f}'.format(depth, acc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deep Neural Network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = MLPClassifier(solver='lbfgs', alpha=1e-5,\n",
    "                    hidden_layer_sizes=2 * [75], random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.9 s, sys: 567 ms, total: 2.46 s\n",
      "Wall time: 455 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "MLPClassifier(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,\n",
       "              beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "              hidden_layer_sizes=[75, 75], learning_rate='constant',\n",
       "              learning_rate_init=0.001, max_fun=15000, max_iter=200,\n",
       "              momentum=0.9, n_iter_no_change=10, nesterovs_momentum=True,\n",
       "              power_t=0.5, random_state=10, shuffle=True, solver='lbfgs',\n",
       "              tol=0.0001, validation_fraction=0.1, verbose=False,\n",
       "              warm_start=False)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time model.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0,\n",
       "       1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0,\n",
       "       0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1,\n",
       "       0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1,\n",
       "       0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred = model.predict(X)\n",
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y, pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Keras (instead of TensorFlow 1.x example)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.models import Sequential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.random.set_seed(1)\n",
    "np.random.seed(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(64, activation='relu', input_dim=features))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "model.compile(loss='binary_crossentropy', optimizer='rmsprop',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.6876, -0.7976],\n",
       "       [-0.4312, -0.7606],\n",
       "       [-1.4393, -1.2363],\n",
       "       [ 1.118 , -1.8682],\n",
       "       [ 0.0502,  0.659 ]])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7fe874652350>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X, y, epochs=50, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100/100 [==============================] - 0s 631us/sample - loss: 0.2416 - accuracy: 0.9100\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.24161519825458527, 0.91]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = np.where(model.predict(X) > 0.5, 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0,\n",
       "       0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0,\n",
       "       0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1,\n",
       "       0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1,\n",
       "       0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred.flatten()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.6876, -0.7976],\n",
       "       [-0.4312, -0.7606],\n",
       "       [-1.4393, -1.2363],\n",
       "       [ 1.118 , -1.8682],\n",
       "       [ 0.0502,  0.659 ]])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.2881, -0.5489],\n",
       "       [-0.3384, -0.5216],\n",
       "       [-1.1122, -0.873 ],\n",
       "       [ 0.8509, -1.3399],\n",
       "       [ 0.0312,  0.5273]])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xs = preprocessing.StandardScaler().fit_transform(X)  \n",
    "Xs[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.7262, 0.3563],\n",
       "       [0.3939, 0.3613],\n",
       "       [0.2358, 0.2973],\n",
       "       [0.6369, 0.2122],\n",
       "       [0.4694, 0.5523]])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xm = preprocessing.MinMaxScaler().fit_transform(X)  \n",
    "Xm[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.6791, -0.3209],\n",
       "       [-0.3618, -0.6382],\n",
       "       [-0.5379, -0.4621],\n",
       "       [ 0.3744, -0.6256],\n",
       "       [ 0.0708,  0.9292]])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xn1 = preprocessing.Normalizer(norm='l1').transform(X)  \n",
    "Xn1[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.9041, -0.4273],\n",
       "       [-0.4932, -0.8699],\n",
       "       [-0.7586, -0.6516],\n",
       "       [ 0.5135, -0.8581],\n",
       "       [ 0.076 ,  0.9971]])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xn2 = preprocessing.Normalizer(norm='l2').transform(X)  \n",
    "Xn2[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "markers = ['o', '.', 'x', '^', 'v']\n",
    "data_sets = [X, Xs, Xm, Xn1, Xn2]\n",
    "labels = ['raw', 'standard', 'minmax', 'norm(1)', 'norm(2)']\n",
    "for x, m, l in zip(data_sets, markers, labels):\n",
    "    plt.scatter(x=x[:, 0], y=x[:, 1], c=y,\n",
    "            marker=m, cmap='coolwarm', label=l)\n",
    "plt.legend();\n",
    "# plt.savefig('../../images/ch13/ml_plot_06.png');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.6876, -0.7976],\n",
       "       [-0.4312, -0.7606],\n",
       "       [-1.4393, -1.2363],\n",
       "       [ 1.118 , -1.8682],\n",
       "       [ 0.0502,  0.659 ]])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0.],\n",
       "       [0., 0.],\n",
       "       [0., 0.],\n",
       "       [1., 0.],\n",
       "       [1., 1.]])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xb = preprocessing.Binarizer().fit_transform(X)  \n",
    "Xb[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2 ** 2  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 1],\n",
       "       [1, 1],\n",
       "       [0, 0],\n",
       "       [3, 0],\n",
       "       [2, 2]])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xd = np.digitize(X, bins=[-1, 0, 1])  \n",
    "Xd[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "4 ** 2  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train-Test Splits "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x, test_x, train_y, test_y = train_test_split(X, y, test_size=0.33,\n",
    "                                                    random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = SVC(C=1, kernel='linear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='linear',\n",
       "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "    tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_x, train_y)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_train = model.predict(train_x)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9402985074626866"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(train_y, pred_train)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_test = model.predict(test_x)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True, False, False, False,  True,  True,  True, False, False,\n",
       "       False,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True, False,  True])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_y == pred_test  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7878787878787878"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(test_y, pred_test)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_c = test_x[test_y == pred_test]\n",
    "test_f = test_x[test_y != pred_test]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(x=test_c[:, 0], y=test_c[:, 1], c=test_y[test_y == pred_test],\n",
    "            marker='o', cmap='coolwarm')\n",
    "plt.scatter(x=test_f[:, 0], y=test_f[:, 1], c=test_y[test_y != pred_test],\n",
    "            marker='x', cmap='coolwarm');\n",
    "# plt.savefig('../../images/ch13/ml_plot_07.png');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "bins = np.linspace(-4.5, 4.5, 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xd = np.digitize(X, bins=bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[34, 21],\n",
       "       [23, 21],\n",
       "       [17, 18],\n",
       "       [31, 15],\n",
       "       [25, 29]])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xd[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x, test_x, train_y, test_y = train_test_split(Xd, y, test_size=0.33,\n",
    "                                                    random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  kernel | accuracy\n",
      "--------------------\n",
      "  linear |    0.848\n",
      "    poly |    0.818\n",
      "     rbf |    0.788\n",
      " sigmoid |    0.455\n"
     ]
    }
   ],
   "source": [
    "print('{:>8s} | {:8s}'.format('kernel', 'accuracy'))\n",
    "print(20 * '-')\n",
    "for kernel in ['linear', 'poly', 'rbf', 'sigmoid']:\n",
    "    model = SVC(C=1, kernel=kernel)\n",
    "    model.fit(train_x, train_y)\n",
    "    acc = accuracy_score(test_y, model.predict(test_x))\n",
    "    print('{:>8s} | {:8.3f}'.format(kernel, acc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>\n",
    "\n",
    "<a href=\"http://tpq.io\" target=\"_blank\">http://tpq.io</a> | <a href=\"http://twitter.com/dyjh\" target=\"_blank\">@dyjh</a> | <a href=\"mailto:training@tpq.io\">training@tpq.io</a>"
   ]
  }
 ],
 "metadata": {
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   "codemirror_mode": {
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